{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f1b9d2fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Xie_PQ\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Xie_PQ\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=5.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Xie_PQ\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Xie_PQ\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=5.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Xie_PQ\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Xie_PQ\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=5.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Xie_PQ\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Xie_PQ\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=5.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Xie_PQ\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Xie_PQ\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=5.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Xie_PQ\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Xie_PQ\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=5.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Xie_PQ\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Xie_PQ\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=5.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Xie_PQ\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Xie_PQ\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=5.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Xie_PQ\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Xie_PQ\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=5.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "反标准化后的聚类中心:\n",
      "         性别       年龄段       BMI       手术史       既往史      是否吸烟      是否酗酒  \\\n",
      "0  0.630435  2.166667  1.565217  0.304348  0.608696   0.07971  0.086957   \n",
      "1       1.0  2.165984  1.481557  0.059426  0.280738  0.004098  0.022541   \n",
      "2  0.006579  2.182018  1.644737  0.050439  0.269737   0.58114  0.335526   \n",
      "3  0.612403   2.20155  1.403101  4.651163  4.782946  0.155039  0.046512   \n",
      "4  0.451613  2.419355   1.16129  0.258065  0.548387   0.16129   0.16129   \n",
      "\n",
      "      镇静药名称 术中不良反应严重程度  呕吐问题严重程度 头部问题情况严重程度 精神状态情况严重程度 腹部问题情况严重程度 其他不良反应严重程度  \n",
      "0  0.427536   0.224638  0.884058   1.101449   0.956522   0.565217       -0.0  \n",
      "1  0.653689   0.143443  0.038934   0.034836   0.081967    0.05123       -0.0  \n",
      "2  0.653509   0.195175  0.019737   0.050439   0.059211   0.050439        0.0  \n",
      "3  0.573643   0.139535  0.100775   0.077519   0.085271   0.069767        0.0  \n",
      "4  0.548387    0.16129  0.290323   0.451613   0.451613   0.451613        1.0  \n",
      "Calinski-Harabasz Score: 150.75361123021978\n",
      "Silhouette Score: 0.15535455483794083\n",
      "Davies-Bouldin Score: 1.7669888020430857\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Xie_PQ\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Xie_PQ\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=5.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>分类</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1237</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1238</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1239</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1240</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1241</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1242 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      分类\n",
       "0      1\n",
       "1      1\n",
       "2      2\n",
       "3      0\n",
       "4      1\n",
       "...   ..\n",
       "1237   3\n",
       "1238   1\n",
       "1239   1\n",
       "1240   1\n",
       "1241   1\n",
       "\n",
       "[1242 rows x 1 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import calinski_harabasz_score, silhouette_score, davies_bouldin_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "\n",
    "# 读取表格数据\n",
    "data = pd.read_excel('Kmeans++数据集.xlsx', index_col=0)\n",
    "\n",
    "# 提取特征列和样本行\n",
    "features = data.columns\n",
    "samples = data.index\n",
    "\n",
    "# 标准化\n",
    "scaler = StandardScaler()\n",
    "data = scaler.fit_transform(data)\n",
    "\n",
    "# 初始化空列表\n",
    "chs = []\n",
    "sse_values = []\n",
    "sil_scores = []\n",
    "db_scores = []\n",
    "\n",
    "# 进行不同K值的聚类分析\n",
    "k_values = range(2,11)  # K值的范围：根据实际情况确定聚类范围\n",
    "for k in k_values:\n",
    "    # 使用kmeans++聚类算法\n",
    "    kmeans = KMeans(n_clusters=k, init='k-means++', random_state=2024)\n",
    "    kmeans.fit(data)\n",
    "    sse_values.append(kmeans.inertia_)\n",
    "    \n",
    "    # 计算Calinski-Harabasz分数\n",
    "    ch = calinski_harabasz_score(data, kmeans.labels_)\n",
    "    chs.append(ch)\n",
    "    \n",
    "    # 计算轮廓系数\n",
    "    sil_score = silhouette_score(data, kmeans.labels_)\n",
    "    sil_scores.append(sil_score)\n",
    "    \n",
    "    # 计算Davies-Bouldin分数\n",
    "    db_score = davies_bouldin_score(data, kmeans.labels_)\n",
    "    db_scores.append(db_score)\n",
    "\n",
    "# ******************绘制肘部法则的折线图*******************\n",
    "plt.plot(k_values, sse_values,'r-')\n",
    "plt.xlabel('Number of clusters K')\n",
    "plt.ylabel('distortions')\n",
    "plt.scatter(k_values, sse_values, marker='^', color='blue')\n",
    "plt.xticks(range(0, 11, 2))\n",
    "plt.show()\n",
    "\n",
    "# *********绘制不同K值下的Calinski-Harabasz柱状图**********\n",
    "plt.bar(k_values, chs)\n",
    "plt.xlabel('Number of clusters K')\n",
    "plt.ylabel('Calinski-Harabasz Score')\n",
    "plt.ylim(0, 200) \n",
    "plt.xticks(range(0, 11, 2))\n",
    "# 待改进：将最大的chs的柱子改成红色\n",
    "plt.bar(5, chs[3], color='red')   \n",
    "# 标注每个数据点的数值\n",
    "for i in range(len(chs)):\n",
    "    plt.text(k_values[i], chs[i], f'{chs[i]:.0f}', ha='center', va='bottom', fontsize=6)\n",
    "plt.show()\n",
    "\n",
    "# 根据Calinski-Harabasz指标选择最佳K值\n",
    "best_k = k_values[np.argmax(chs)]\n",
    "\n",
    "# 根据最佳K值重新进行聚类\n",
    "best_kmeans = KMeans(n_clusters=best_k, init='k-means++', random_state=42)\n",
    "best_kmeans.fit(data)\n",
    "\n",
    "\n",
    "# 反标准化聚类中心\n",
    "cluster_centers = pd.DataFrame(scaler.inverse_transform(best_kmeans.cluster_centers_), columns=features)\n",
    "\n",
    "# 保留前6位小数并转换为字符串格式\n",
    "cluster_centers = cluster_centers.round(6).astype(str)\n",
    "\n",
    "# 打印反标准化后的聚类中心\n",
    "print(\"反标准化后的聚类中心:\")\n",
    "print(cluster_centers)\n",
    "\n",
    "\n",
    "# 计算最佳K值下的CHI、轮廓系数和Davies-Boeldin的值\n",
    "best_ch = calinski_harabasz_score(data, best_kmeans.labels_)\n",
    "best_sil_score = silhouette_score(data, best_kmeans.labels_)\n",
    "best_db_score = davies_bouldin_score(data, best_kmeans.labels_)\n",
    "# 打印结果\n",
    "print(\"Calinski-Harabasz Score:\", best_ch)\n",
    "print(\"Silhouette Score:\", best_sil_score)\n",
    "print(\"Davies-Bouldin Score:\", best_db_score)\n",
    "\n",
    "# 将分类结果保存到另一个表格\n",
    "# 待改进——>保存到另外一个表格：第一列：样本；第二列：分类\n",
    "cluster_labels = pd.DataFrame(best_kmeans.labels_, columns=['分类']) \n",
    "cluster_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1e3133f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Xie_PQ\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Xie_PQ\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=5.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Xie_PQ\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Xie_PQ\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=5.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Xie_PQ\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Xie_PQ\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=5.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Xie_PQ\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Xie_PQ\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=5.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Xie_PQ\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Xie_PQ\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=5.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Xie_PQ\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Xie_PQ\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=5.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Xie_PQ\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Xie_PQ\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=5.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Xie_PQ\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Xie_PQ\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=5.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Xie_PQ\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Xie_PQ\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=5.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Xie_PQ\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Xie_PQ\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=5.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Elbow Plot'}, xlabel='Number of clusters', ylabel='Sum of Squared Errors'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.cluster import KMeans\n",
    "import scikitplot as skplt\n",
    "\n",
    "# 读取表格数据\n",
    "data = pd.read_excel('Kmeans++数据集.xlsx', index_col=0)\n",
    "\n",
    "\n",
    "# 标准化\n",
    "scaler = StandardScaler()\n",
    "data = scaler.fit_transform(data)\n",
    "\n",
    "kmeans = KMeans(random_state=1)\n",
    "skplt.cluster.plot_elbow_curve(kmeans,data, cluster_ranges=range(1, 11))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eddc55d8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
